Non-contact assessment of cardiac physiology using FO-MVSS-based ballistocardiography: a promising approach for heart failure evaluation

Continuous monitoring of cardiac motions has been expected to provide essential cardiac physiology information on cardiovascular functioning. A fiber-optic micro-vibration sensing system (FO-MVSS) makes it promising. This study aimed to explore the correlation between Ballistocardiography (BCG) waveforms, measured using an FO-MVSS, and myocardial valve activity during the systolic and diastolic phases of the cardiac cycle in participants with normal cardiac function and patients with congestive heart failure (CHF). A high-sensitivity FO-MVSS acquired continuous BCG recordings. The simultaneous recordings of BCG and electrocardiogram (ECG) signals were obtained from 101 participants to examine their correlation. BCG, ECG, and intracavitary pressure signals were collected from 6 patients undergoing cardiac catheter intervention to investigate BCG waveforms and cardiac cycle phases. Tissue Doppler imaging (TDI) measured cardiac time intervals in 51 participants correlated with BCG intervals. The BCG recordings were further validated in 61 CHF patients to assess cardiac parameters by BCG. For heart failure evaluation machine learning was used to analyze BCG-derived cardiac parameters. Significant correlations were observed between cardiac physiology parameters and BCG's parameters. Furthermore, a linear relationship was found betwen IJ amplitude and cardiac output (r = 0.923, R2 = 0.926, p < 0.001). Machine learning techniques, including K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost, respectively, demonstrated remarkable performance. They all achieved average accuracy and AUC values exceeding 95% in a five-fold cross-validation approach. We establish an electromagnetic-interference-free and non-contact method for continuous monitoring of the cardiac cycle and myocardial contractility and measure the different phases of the cardiac cycle. It presents a sensitive method for evaluating changes in both cardiac contraction and relaxation in the context of heart failure assessment.


Study population and protocol
The study population was represented by participants that were recruited across the four distinct stages (Supplementary Fig. S7).
In the first stage, 10 participants with normal cardiac function (age range: 20-25 years, 7 males, 3 females) were selected from 111 participants recruited with normal cardiac function to assess the stability and consistency of the BCG signals obtained using the FO-MVSS.Eligible participants were required to have no history of cardiovascular or respiratory diseases and demonstrate the ability to adhere to the experimental instructions.Participants who did not meet these criteria were excluded from the study.The specific exclusion criteria were as follows: (1) Participants with tremors, such as Parkinson's disease.(2) Participants are unwilling and unable to adhere to the following instructions and cooperate during the experiment.
In the second stage, the study involved participants with normal cardiac function to analyze the correlation between BCG and cardiac motion.In the experiment, the inclusion criteria were as follows: (1) participants without any significant cardiac, respiratory, or physical conditions or discomfort were selected.(2) Participants were required to remain calm during the recordings and comply with the experimental instructions.There were 101 participants (age range: 20-78 years, 55 males, 46 females) who were selected from the 111 participants recruited with normal cardiac function in this stage.
Simultaneous BCG and electrocardiogram (ECG) recordings were obtained to investigate the correlation between the BCG waveforms and cardiac motion.
In the third stage, the study aimed to analyze the relationship between BCG waveforms and the phases of the cardiac cycle and validated the effectiveness of BCG-based cardiac cycle phases categorization.The first experiment in this third stage involved 8 patients selected from the 111 participants recruited with normal cardiac function.These patients were required to undergo cardiac catheterization and accept simultaneous collection of BCG signals.It was essential for these patients to fully cooperate with experimental instructions.Participants with incomplete experimental records were excluded.Finally, a total of 6 patients (age range: 33-55 years, 4 males, 2 females) were included in this analysis.We collected the BCG, ECG, and intracavitary pressure signals to analyze their relationship with the phases of the cardiac cycle.The comparison of the characteristics from the BCG waveforms with the different phases of the cardiac cycle was achieved, to hypothesize a BCG-based cardiac cycle staging method.
In the second and third parts of this stage, our study involved medical validation and incorporated two sub-experiments to assess the accuracy of the BCG-based cardiac cycle phase categorization.The aim was to provide a pathological perspective and a preliminary analysis of the clinical application of the BCG-based cardiac cycle phase categorization.
FO-MVSS circuit.The microcontroller (STM32H743) of the FO-MVSS (Fig. 1) was set at a sampling frequency of 1024 Hz.Firstly, the optical signal modulated by the vibration was converted into an electrical signal using a photodetector (PD).Secondly, the electrical signal was sent to pre-amplifier 1, which performed high-pass filtering (second-order active high-pass filter, cut-off frequency: 0.1 Hz) with 100 times amplification.The output contained both the respiratory and heartbeat signal components.
Thirdly, the signal was subjected to further high-pass filtering (second-order active high-pass filter, cut-off frequency: 0.3 Hz) with 20 times amplification in pre-amplifier 2.Then, the output was converted into a digital signal by an A/D convertor.Finally, the processed digital signal was transmitted to the computer.

Supplementary Tables
validation.There were 51 participants who were randomly selected from the 111 participants recruited (age range: 25-45 years, 41 males, 10 females) and underwent TDI classification 21 to extract cardiac time parameters corresponding to different phases of the cardiac cycle.The extracted cardiac time parameters were then compared with the corresponding cardiac time intervals 21 obtained from the BCG signals to validate the accuracy of the BCG-based cardiac phase categorization.(b) Waveform amplitude and CO association validation.For this analysis, 11 young participants (age range: 20-30 years, 11 males) were selected from the 111 recruited participants with normal cardiac function.Participants were required to provide rest and exercise BCG and M-mode Doppler ultrasound data.The analysis focused on investigating the association between the changes in waveform amplitudes within the ejection phase of the BCG signal and the cardiac output.The aim was to further validate the accuracy of the BCG-based cardiac cycle phase categorization.In the fourth stage, 61 patients with CHF (age range: 48-76 years, 27 males, 34 females) were recruited for evaluating the potential of the BCG-based cardiac cycle phases.BCG recordings were collected, and the association between the time parameters and amplitude parameters involved in the BCG-based cardiac cycle phase categorization and the left ventricular ejection fraction was evaluated.

Table S4 . Overview of Hyperparameters for Classification Models in the Study Model Hyperparameters Value SVM
Logistic regression.SVM, support vector machines; KNN, K-nearest neighbor;DTC, Decision tree.RF, Radom forest.XGBoost, Extrme gradient boosting.P, the power parameter for the minkowski distance.C, the inverse of regularization strength, and higher values indicate weaker regularization.